Contingent screening for down syndrome is an efficient alternative to non-disclosure sequential screening

Objective To present a first and second trimester Down syndrome screening strategy, whereby second-trimester marker determination is contingent on the first-trimester results. Unlike non-disclosure sequential screening (the Integrated test), which requires all women to have markers in both trimesters, this allows a large proportion of the women to complete screening in the first trimester. Methods Two first-trimester risk cut-offs defined three types of results: positive and referred for early diagnosis; negative with screening complete; and intermediate, needing second-trimester markers. Multivariate Gaussian modelling with Monte Carlo simulation was used to estimate the false-positive rate for a fixed 85% detection rate. The false-positive rate was evaluated for various early detection rates and early test completion rates. Model parameters were taken from the SURUSS trial. Results Completion of screening in the first trimester for 75% of women resulted in a 30% early detection rate and a 55% second trimester detected rate (net 85%) with a false-positive rate only 0.1% above that achievable by the Integrated test. The screen-positive rate was 0.1% in the first trimester and 4.7% for those continuing to be tested in the second trimester. If the early detection rate were to be increased to 45% or the early completion rate were to be increased to 80%, there would be a further 0.1% increase in the false-positive rate. Conclusion Contingent screening can achieve results comparable with the Integrated test but with earlier completion of screening for most women. Both strategies need to be evaluated in large-scale prospective studies particularly in relation to psychological impact and practicability.

Remote, subsurface detection of the algal toxin domoic acid onboard the Environmental Sample Processor: Assay development and field trials

The ability to detect harmful algal bloom (HAB) species and their toxins in real- or near real-time is a critical need for researchers studying HAB/toxin dynamics, as well as for coastal resource managers charged with monitoring bloom populations in order to mitigate their wide ranging impacts. The Environmental Sample Processor (ESP), a robotic electromechanical/fluidic system, was developed for the autonomous, subsurface application of molecular diagnostic tests and has successfully detected several HAB species using DNA probe arrays during field deployments. Since toxin production and thus the potential for public health and ecosystem effects varies considerably in natural phytoplankton populations, the concurrent detection of HAB species and their toxins onboard the ESP is essential. We describe herein the development of methods for extracting the algal toxin domoic acid (DA) from Pseudonitzschia cells (extraction efficiency >90%) and testing of samples using a competitive ELISA onboard the ESP. The assay detection limit is in the low ng/mL range (in extract), which corresponds to low ng/L levels of DA in seawater for a 0.5 L sample volume acquired by the ESP. We also report the first in situ detection of both a HAB organism (i.e., Pseudo-nitzschia) and its toxin, domoic acid, via the sequential (within 2-3 h) conduct of species- and toxin-specific assays during ESP deployments in Monterey Bay, CA, USA. Efforts are now underway to further refine the assay and conduct additional calibration exercises with the aim of obtaining more reliable, accurate estimates of bloom toxicity and thus their potential impacts. Published by Elsevier B.V.

k-fold Subsampling based Sequential Backward Feature Elimination

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy